AI Security Checklist: Don't let your AI go rogue

AI Application Security, or AI AppSec, is broken down into four key areas: input and output filtering and validation, red team tests for security and AI, enforcing content safety policies, and creating an incident response plan. These components collectively address the fundamental security dimensions — defense, offense, compliance, and incident response combined with monitoring.

Implementing Input and Output Filtering and Validation

Input and output filtering and validation serve as critical defensive measures. For an AI application to be considered secure under this criterion, it must have AI firewalls and guardrails. Since no two AI applications are alike, each requires a custom configuration of these defensive tools. For instance, a health AI assistant might need strict safeguards against disclosing personal health information and ensuring the accuracy of the information provided, while a car sales chatbot might focus more on preventing competitor mentions.

It’s essential to consider the specific risks and regulatory requirements of the industry in which the AI application operates. These might include preventing toxic language, avoiding negative comments about your company, or addressing potential prompt injections that could lead to model or context leakage. Understanding the nuances of trustworthy AI applications is critical for maintaining integrity and trust; more on this can be found in our previous article, “Mission Possible: Trustworthy GenAI”.

Running Red Team Tests

On the offensive side of security, AI AppSec involves automated and continuous AI red-teaming — or AI pen-testing. Why Automation? — Automation is crucial here to manage the broad spectrum of potential vulnerabilities both time and cost efficiently. Otherwise it would require a dedicated AI security team pen-testing your AI applications manually. It’s also vital for these tests to be continuous, especially since AI applications are frequently updated. Each update, whether it’s a system prompt adjustment, a guardrail config modification, or a complete model replacement, could potentially introduce new vulnerabilities.

For those interested in deeper insights into AI red teaming, our latest blog, “Intro to Red Teaming LLMs: A Proactive Shield for Chatbots and Beyond” provides a comprehensive introduction.

Enforcing Content Safety Policies

To mitigate the risk of AI-generated harmful content, it’s necessary to enforce strict content safety policies continuously. These policies must be aligned with the latest applicable legislation, considering the type of AI application, the industry, the geographical region, and the architectural framework. Partnering with AI Compliance experts can streamline the compliance process, ensuring your application meets all regulatory requirements.

Creating an Incident Response Plan

An effective incident response plan starts with the capability to detect issues before they escalate, ideally not through a crisis caused by a social media post. Monitoring within your AI application can provide essential situational awareness, allowing you to respond swiftly and effectively to potential incidents. Useful inputs for this process include continuous reports from proactive AI pen-testing efforts, which simulate attacks and help prepare your team to handle real-world vulnerabilities.

In conclusion, securing generative AI applications is critical, not only to protect sensitive data but also to maintain trust and compliance in a rapidly evolving digital landscape.